Joint extraction of entities and relations using multi-label tagging and relational alignment

Relation extraction aims to identify semantic relations between entities in text. In recent years, this task has been extended to the joint extraction of entities and relations, which requires the simultaneous identification of entities and their relations from sentences. However, existing methods, limited by the existing tagging scheme, fail to identify more complex entities, which in turn limits the performance of the joint extraction task. This article presents a joint extraction model for entities and relations called MLRA-LSTM-CRF that uses multi-label tagging and relational alignment to transform this task into a multi-label tag recognition problem. The proposed model first tags the entities and their relations according to the multi-label tagging scheme and then uses a joint entity and relation extraction module with a multi-layer attention mechanism to extract the triplets in the sentence. Finally, the relational alignment module is used to align the predicted relation classification results. Experimental results on the New York Times and Wiki-KBP datasets indicate that MLRA-LSTM-CRF is significantly better than that of several state-of-the-art models and baseline.